自适应谱减声发射消噪及轴承故障诊断

doi:10.16180/j.cnki.issn1007-7820.2019.04.004

Abstract

Abstract:

When acoustic emission signals are analyzed for bearings, environmental noise causes signal energy to fluctuate, resulting in misdiagnosis of late failures. To solve the problem, a spectral subtraction method was introduced to pre-noise the acoustic emission signal to enhance the stability of signal. In view of the lack of processing performance of spectral subtraction for non-stationary signals, spectral subtraction coefficients were modified and genetic algorithm was used to optimize the spectral subtraction coefficients(m, λ) globally. Experiment showed that the adaptive spectral subtraction method could obtain better spectral subtraction coefficients, and the fluctuation of wavelet packet energy feature could be eliminated. After the SVM training for the energy features of various bearing faults, the accuracy rate could reach about 92%.

Key words: fault diagnosis, acoustic emission, spectral subtraction, genetic algorithm, wavelet packet transform, SVM

CLC Number:

TP368.1

ZHU Wangchun,CHENG Hao,GAO Haiying. Adaptive Acoustic Emission Noise Elimination and Bearing Fault Diagnosis[J].Electronic Science and Technology, 2019, 32(4): 16-20.

Figures/Tables 6

Table 1

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Figure 2.

Figure 3.

Table 3

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SNR_i	m_B	λ_B	ρ
-12 dB	2.52	3.25	0.872
-11 dB	2.49	3.27	0.871
-10 dB	2.53	3.25	0.875
-9 dB	2.47	3.23	0.874
?	?	?	?
0 dB	2.23	3.11	0.870
?	?	?	?
9dB	1.94	2.02	0.863
10dB	1.92	2.04	0.871

优化方法	SNR_i	m_B	λ_B	ρ
实验优化	-10 dB	2.5	3.0	0.827
	0 dB	2.5	3.0	0.813
	10 dB	2.0	2.0	0.836
遗传算法优化	-10 dB	2.53	3.25	0.875
	0 dB	2.23	3.11	0.870
	10 dB	1.94	2.04	0.871

轴承状态类型	正常	内圈故障	外圈故障
消噪前准确率	61.4%	57.6%	54.5%
消噪后准确率	93.5%	90.3%	91.8%

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Adaptive Acoustic Emission Noise Elimination and Bearing Fault Diagnosis

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